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The phenomenon of toll evasion on expressway is universal and recognition of evasion phenomena has become a necessary means to reduce property losses. In view of this situation, a clustering method based Gaussian mixture model(GMM) of load weight is applied to identify the toll evasion by transportation vehicles. Firstly, based on the Kolmogorov-Smirnov and Quantile-Quantile plot test of the load weight in different driving cycles, it is definite that the load in a certain driving cycle is approximately Gaussian mixture distribution(GMD) and there are significant differences among load distributions in different driving cycles. Then, the load during historical vehicles in a certain driving cycle is clustered by GMM. Expectation–maximization(EM) algorithm is used to calculate the parameters of GMM. Based on the clustering results, the GMD of the load in a certain driving cycle is clear. Finally, according to thecriterion of Gaussian distribution, we scientifically obtain a reasonable load interval and distinguish the toll evasion by transportation vehicles. In addition, the concrete practice procedures of the toll evasion recognition method are discussed. Empirical results demonstrate that could achieve satisfactory results when the proposed method is applied to model the toll data of six-axis trucks of south of Baoding City station in Hebei province of China.

A toll evasion recognition method based on Gaussian mixture clustering